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Found 14 papers, 4 papers with code
Supervised structure learning
This paper concerns structure learning or discovery of discrete generative models.
Hierarchical generative modelling for autonomous robots
We approach this problem by hierarchical generative modelling equipped with multi-level planning-for autonomous task completion-that mimics the deep temporal architecture of human motor control.
On efficient computation in active inference
Despite being recognized as neurobiologically plausible, active inference faces difficulties when employed to simulate intelligent behaviour in complex environments due to its computational cost and the difficulty of specifying an appropriate target distribution for the agent.
Bistable perception, precision and neuromodulation
Bistable perception follows from observing a static, ambiguous, (visual) stimulus with two possible interpretations.
Modelling non-reinforced preferences using selective attention
These memories are selectively attended to, using attention and gating blocks, to update agent's preferences.
Active inference, Bayesian optimal design, and expected utility
Conversely, active inference reduces to Bayesian decision theory in the absence of ambiguity and relative risk, i. e., expected utility maximization.
Active Inference for Stochastic Control
Active inference has emerged as an alternative approach to control problems given its intuitive (probabilistic) formalism.
Bayesian brains and the Rényi divergence
Under the Bayesian brain hypothesis, behavioural variations can be attributed to different priors over generative model parameters.
Exploration and preference satisfaction trade-off in reward-free learning
In this paper, we pursue the notion that this learnt behaviour can be a consequence of reward-free preference learning that ensures an appropriate trade-off between exploration and preference satisfaction.
Reward Maximisation through Discrete Active Inference
Precisely, we show the conditions under which active inference produces the optimal solution to the Bellman equation--a formulation that underlies several approaches to model-based reinforcement learning and control.
Deep active inference agents using Monte-Carlo methods
In a more complex Animal-AI environment, our agents (using the same neural architecture) are able to simulate future state transitions and actions (i. e., plan), to evince reward-directed navigation - despite temporary suspension of visual input.
Active inference: demystified and compared
In this paper, we provide: 1) an accessible overview of the discrete-state formulation of active inference, highlighting natural behaviors in active inference that are generally engineered in RL; 2) an explicit discrete-state comparison between active inference and RL on an OpenAI gym baseline.
Predicting Language Recovery after Stroke with Convolutional Networks on Stitched MRI
One third of stroke survivors have language difficulties.
